National Taiwan University Hanne Inez Wolff Aug. 21st, 2013

1. Trans-Sialidase’sRole in Chronic Chagas Disease, and it’s Potential for Infection Inhibition by Employing Natural Products National Taiwan University Hanne Inez Wolff Aug. 21st, 2013

2. Research Proposal • Chagas Disease is affecting 10-15 million people world wide, especially in rural areas of Latin America. There are two phases to the disease; the acute phase which lasts about 4-8 weeks, and the chronic phase which follows the acute phase. Currently, there are only two drugs to treat Chagas disease, which are both not approved by the FDA, and to get a hold of the drugs, infected individuals must request them through the CDC and follow strict protocols. More importantly, these two drugs, despite their serious side effects, have shown efficiency only in infected individuals with acute Chagas Disease. • My research is looking at potential Natural Products to treat chronic Chagas Disease via utilizing computational modeling and predictions.

3. Methodology • Screened and identified “good” PDB structures for docking. • “Good” is defined as experimental and docking overlap • Two docking programs were utilized: • AutoDock 4.0 with plug in • AutoDockVina • Three Trans-Sialidasestructures were identified as good representations in the PDB: 1MS9, 1MS8, 1S0J • Both 1MS9, and 1MS8 are from Buschiazzo (2002) article • 1S0J is from Amaya (2004) article

4. Data Collected from PDB Analysis

5. 1S0J - Trypanosoma cruzi trans-sialidase in complex with MuNANA (Michaelis complex) Experimental Ligand crystal (Yellow) Vina Docking (Red) AutoDock4.0 Docking (Pink)

6. 1MS8 - Triclinic form of Trypanosoma cruzi trans-sialidase, in complex with 3-deoxy-2,3-dehydro-N-acetylneuraminic acid (DANA) Experimental Ligand crystal (Yellow) Vina Docking (Red) AutoDock4.0 Docking (Pink)

7. 1MS9 - Triclinic form of Trypanosoma cruzi trans-sialidase, in complex with lactose Experimental Ligand crystal (Yellow) Vina Docking (Red) AutoDock4.0 Docking (Pink)

8. Results – 1S0J • The best structures were used for docking: • Top Ranking binding energies, 1S0J: • ----------------------------------------------------------------------------------------- • Rank File Name Binding Affinity kcal/mol • ----------------------------------------------------------------------------------------- • 1 TPD.71184833541_out.pdbqt -13.6 • 2 TPD.91185766573_out.pdbqt -12.6 • 3 TPD.281010096476_out.pdbqt -12.3 • 4 TPD.281011317462_out.pdbqt -12.3 • 5 TPD.71184834376_out.pdbqt -12.2 • 6 TPD.281010461515_out.pdbqt -12.1 • 7 TPD.91186211040_out.pdbqt -12.1 • 8 TPD.101185345882_out.pdbqt -12.0 • 9 TPD.281011318172_out.pdbqt -12.0 • 10 TPD.281011317061_out.pdbqt -11.8

9. Results – 1MS8 • The best structures were used for docking: • Top Ranking binding energies, 1MS8: • ----------------------------------------------------------------------------------------- • Rank File Name Binding Affinity kcal/mol • ----------------------------------------------------------------------------------------- • 1 TPD.91185766573_out.pdbqt -14.4 • 2 TPD.281010096476_out.pdbqt -13.8 • 3 TPD.71184833541_out.pdbqt -12.7 • 4 TPD.281011409415_out.pdbqt -12.4 • 5 TPD.281011409716_out.pdbqt -12.4 • 6 TPD.71184834376_out.pdbqt -12.3 • 7 TPD.91186211040_out.pdbqt -11.7 • 8 TPD.281011297903_out.pdbqt -11.6 • 9 TPD.281011398209_out.pdbqt -11.6 • 10 TPD.281011399613_out.pdbqt -11.6

10. Results – 1MS9 • The best structures were used for docking: • Top Ranking binding energies, 1MS9: • ----------------------------------------------------------------------------------------- • Rank File Name Binding Affinity kcal/mol • ----------------------------------------------------------------------------------------- • 1 TPD.91185766573_out.pdbqt -14.4 • 2 TPD.281010096476_out.pdbqt -13.8 • 3 TPD.71184833541_out.pdbqt -12.7 • 4 TPD.281011409415_out.pdbqt -12.4 • 5 TPD.281011409716_out.pdbqt -12.4 • 6 TPD.71184834376_out.pdbqt -12.3 • 7 TPD.91186211040_out.pdbqt -11.7 • 8 TPD.281011297903_out.pdbqt -11.6 • 9 TPD.281011398209_out.pdbqt -11.6 • 10 TPD.281011399613_out.pdbqt -11.6

11. Top 3 Results • Consistently these three compounds are ranked top three across the different trans-sialidasepdb files: • dihalenaquinolide A (CSN: 71184833541) • (+)-Ovigeridimerin (CSN: 91185766573) • Bisisodiospyrin (CSN: 281010096476)

12. DihalenaquinolideA • Catalogue number for Taiwanese Pharmaceutical Database, CSN: 71184833541 • Vina docking result: -13.6 kcal/mol • AutoDock 4.0 docking result: -15.23 kcal/mol • Agreement between Vina and AutoDock 4.0 alignment

13. Vina (Yellow) AutoDock 4.0 (Red)

14. Vina (Yellow) AutoDock 4.0 (Red)

15. (+)-Ovigeridimerin • Catalogue number for Taiwanese Pharmaceutical Database, CSN: 91185766573 • Vina docking result: -12.6 kcal/mol • AutoDock 4.0 docking result: -13.5 kcal/mol

16. Vina (Yellow) AutoDock 4.0 (Red)

17. Vina (Yellow) AutoDock 4.0 (Red)

18. Bisisodiospyrin • Catalogue number for Taiwanese Pharmaceutical Database, CSN: 281010096476 • Vina docking result: -12.3 kcal/mol • AutoDock 4.0 docking result: -15.73 kcal/mol

19. Vina (Red) AutoDock 4.0 (Pink)

20. Vina (Red) AutoDock 4.0 (Pink)

21. Cultural Fun

22. Woai Taiwan

23. I Would Like to Extend a Thanks to... University of California, San Diego • Gabriele Wienhausen • Peter Arzberger • Dr. Phil Bourne • ChiragKrishna National Taiwan University • Dr. Jung-Hsin Lin • Dr. Jung-Hsin Lin’s lab, and finally Tosh Nomura Eureka! Foundation for making this trip possible